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Measures 3.2 ms Vectors e.g. wind speed and direction: 3.2 ms North
Categorical e.g. ‘cloudy’, ‘windy’ etc. Composite combination of phenomena, e.g. Conductivity, Temperature, Dissolved
oxygen WaterML2.0 – part 1 focuses on timeseries with value types of measures and categories. These
types capture the percentage of requirements for data exchange, while keeping a level of simplicity in the model and encodings, leading to simpler implementations. Composite timeseries
multiple phenomena and other types will be addressed in future versions.
Within the hydrology domain, downstream processes often annotate timeseries using both manual and automatic methods. An example is quality assurance and control where a timeseries
may be marked up to give an indication of quality of data; this may be done, for example, by a person manually looking at a plot, or by algorithms checking for abnormal deviations or other
indicators. Such annotations come in many different forms and are important to persist for data exchange purposes.
9.12.1 Relationship to timeseries concepts in other domains
Timeseries are not specific to the hydrology domain, but the observation processes and use of data form a specific view of timeseries that represent the particular domains requirements. Other
domains have information models for timeseries that reflect the needs of their domain; for example, financial timeseries, other environmental sciences or science domains making use of
continuous observation. This standard, with a focus on assisting interoperability and the growing need for cross-domain exchange, attempts to relate key concepts to those in related domains. The
most closely related concept within the spatial and observation community is that of coverages.
9.12.2 Relationship to ISO19123 – Coverages
ISO19123 defines a coverage as a: “a feature that acts as a function to return values from its range for any direct position within
its spatial, temporal or spatiotemporal domain” Or,
“…a coverage is a feature that has multiple values for each attribute type, where each direct position within the geometric representation of the feature has a single value for each attribute
type.”
A time series in the context of observational data can be seen as a discrete coverage, where the domain is a spatiotemporal axis and the range is all the possible values of the observed property.
An instance of such a coverage would be a set of ordered time instances where each is associated with a single value from the attribute space. This association is often represented using time-
value pairs.
OGC WaterML 2.0 OGC 10-126r3
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The ISO coverages model describes two approaches to representing coverages: a ‘domain-range’ representation where the domain and range are encoded separately, with a mapping function that
allows looking up of the range value for a given domain value; and a ‘geometry-value’, or interleaved, approach whereby the geometry and value are coupled together – the coupling
explicitly represents the mapping.
GML 3.2.1 notes that the geometry-value approach “... is typically used during data collection where a set or properties relating to a single location are managed together, or update of a
datastore where only a small number of features are manipulated at one time.”
And the domain-range approach is ‘…more suitable for analysis, where spatio-temporal patterns and anomalies within a specific property are of interest.”
Within hydrology this is often the case. For example, a grid showing the spatial distribution of rainfall is often generated from observations using interpolation techniques such as kriging. The
surface may be generated using point observations from in-situ sensors. The point observations are often represented using a geometry-value structure with the generated surface being
represented using the domain-range approach, with a spatial grid domain mapped to its range values representing total rainfall in the grid cell, for example. This provides a more efficient
representation.
WaterML2.0 defines a timeseries as a coverage whose domain consists of collection of ordered temporal elements and the spatial component relates to the feature of interest of the observation.
For in-situ timeseries the spatial element will be fixed and need not be directly represented in the timeseries domain. The core coverage elements and the relationship to timeseries are shown in
Figure 16 and Figure 18.
Temporal axis
Domain Parameter
Space
Range
Figure 17 - Timeseries as a coverage
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Figure 18 - Timeseries as a coverage
A timeseries may then be viewed in two ways from a coverage perspective: using the ‘domain- range’ view or the ‘geometry-value’ or interleaved view. Note that the term ‘geometry’ holds the
domain object and is composed of varying spatial and temporal components e.g. time instants. The two types are show in Figure 21 and Figure 22 respectively.
The geometry-value view is consistent with the most common structuring in the hydrology domain: time and values are coupled together and represent discrete observations at time instants.
The use of the term geometry is based on the coverage viewpoint; time-value will be used in place for clarity.
9.12.3 Timeseries and point metadata
